Editor's Note: I again triumphantly wrestled the faiV from Tim Ogden’s clutches this week. Well, actually, he asked me to take over while he’s in transit today. Inspired by this week's amazing Pooh noir Twitter thread, I decided to dedicate this faiV to some powerful investigations (of the journalistic, not private eye, not private eye type). --Jonathan Morduch

1. Crappy Financial Products: The results are no surprise, but it remains troubling to see the numbers. “Color and Credit” is a 2018 revision of a 2017 paper by Taylor Begley and Amitatosh Purnanandam. The subtitle is “Race, Regulation, and the Quality of Financial Services.” Most studies of consumer financial problems look at quantity: the lack of access to financial products. But here the focus is on quality: You can get products, but they’re lousy. Too often, they’re mis-sold, fraudulent, and accompanied by bad customer service. These problems had been hard to see, but they’ve been uncovered via the Consumer Financial Protection Bureau Complaints database, a terrifically valuable, publicly accessible—and freely downloadable—database. (Side note: this makes me very nervous about the CFPB’s current commitment to maintaining the data.)

Thousands of complaints are received each week, and the authors look at 170,000 complaints from 2012-16, restricted to mortgage problems. The complaints come from 16,309 unique zipcodes – and the question is: which zipcodes have the most complaints and why? The first result is that low income and low educational attainment in a zipcode are strongly associated with low quality products. Okay, you already predicted that. On top of those effects, the share of the local population identified as being part of a minority group also predicts low quality. No surprise again, but you might not have predicted the magnitude: The minority-share impact is 2-3 times stronger then the income or education impact (even when controlling for income and education). The authors suspect that active discrimination is at work, citing court cases and mystery shopper exercises which show that black and Hispanic borrowers are pushed toward riskier loans despite having credit scores that should merit better options. So, why? Part of the problem could be that efforts to help the most disadvantaged areas are backfiring. Begley and Purnanandam give evidence that regulation to help disadvantaged communities actually reduces the quality of financial products. The culprit is the Community Reinvestment Act, and the authors argue that by focusing the regs on increasing the quantity of services delivered in certain zipcodes, the quality of those services has been compromised – and much more so in heavily-minority areas. Unintended consequences that ought to be taken seriously.

2. TrumpTown: Another great database. ProPublica is a national resource – a nonprofit newsroom. They’ve been doing a lot of data gathering and number-crunching lately. Four items today are from ProPublica. The first is the geekiest: a just-released, searchable database of 2,475 Trump administration appointees. The team spent a year making requests under the Freedom of Information Act, allowing you to now spend the afternoon getting to know the mid-tier officials who are busily deregulating the US economy. The biggest headline is that, of the 2,475 appointees, 187 had been lobbyists, 125 had worked at (conservative) think tanks, and 254 came out of the Trump campaign. Okay, that’s not too juicy. Still, the database is a resource that could have surprising value, even if it’s not yet clear how. Grad students: have a go at it. (Oh, and I’d like to think that ProPublica would have done something similar if Hilary Clinton was president.)

3. Household Finance (and Inequality): This ProPublica story is much more juicy, and much more troubling. Writing in the Washington Post, ProPublica’s Paul Kiel starts: “A ritual of spring in America is about to begin. Tens of thousands of people will soon get their tax refunds, and when they do, they will finally be able to afford the thing they’ve thought about for months, if not years: bankruptcy.” Kiel continues, “It happens every tax season. With many more people suddenly able to pay a lawyer, the number of bankruptcy filings jumps way up in March, stays high in April, then declines.” Bankruptcy is a last resort, but for many people it’s the only way to get on a better path. Even when straddled with untenable debt, it turns out to be costly to get a fresh start.

The problem will be familiar to anyone who has read financial diaries: the need for big, lumpy outlays can be a huge barrier to necessary action. Bankruptcy lawyers usually insist on being paid upfront (especially for so-called “chapter 7” bankruptcies). The problem is that if the lawyers agreed to be paid later, they fear that their fees would also be wiped away by the bankruptcy decision. So, the lawyers put themselves first. The trouble is that the money involved is sizeable: The lawyers’ costs plus court fees get close to $1500. The irony abounds. Many people tell Kiel that if they could easily come up with that kind of money, then they probably wouldn’t be in the position to go bankrupt. Bankruptcy judges see the problem and are trying to jerry-rig solutions, but nonprofits haven’t yet made this a priority. So, for over-indebted households, waiting to receive tax refunds turns out to be a key strategy.

4. Municipal Finance and Household Finance (and Inequality): In a related vein, check out this Mother Jones/ProPublica investigation of bankruptcy in Chicago. The title says it all: “How Chicago Ticket Debt Sends Black Motorists Into Bankruptcy. A cash-strapped city employs punitive measures to collect from cash-strapped residents — and lawyers benefit.” The focus is on the city’s reliance on fees from parking tickets to help balance the books – which can add up for residents and lead to bankruptcy. Even a single unpaid parking ticket can create havoc for poorer households. The situation is hard not to connect to Ferguson, Missouri, the scene of the riots after the shooting of Michael Brown, where, among other abuses of the citizenry, the city used the courts and police as revenue-generating mechanisms.)

Ticket debt in Chicago is concentrated in areas that are predominantly poor and black, because there isn’t slack to pay the initial tickets, making it more likely that debt results. A fairer system would impose fines on a scale connected to individuals’ income and ability-to-pay. But, for now, we have a decidedly regressive system in which the least-able-to-pay face disproportionately large penalties.

5. Social Investment: The final ProPublica story is a collaboration with the New York Times. Many have reported on the rising cost of drugs, but we don’t often see deep reporting on those who pay the price. The personal stories are both familiar and shocking. Two common threads: many people are too poor to easily pay the drug prices but not so poor that they have access to generous public benefits. They’re caught in between. The result is that individuals end up juggling which medicines to take in the same way that cash-strapped families juggle which bills to pay each month – only with much higher stakes.

A second theme is (again) problems posed by large, lumpy, upfront costs. For example: “…Novo Nordisk, the company that sells her fast-acting insulin, Novolog, and her diabetes medication, Victoza, requires low-income Medicare beneficiaries to first spend $1,000 on drugs in each calendar year before they can qualify for free drugs through its program. In a cruel twist, Ms. Johnson doesn’t have that $1,000 to spend, so she resorts to not taking some drugs for months until she reaches the company’s threshold.” The stories highlight ways in which health problems are often financial problems.

In a related way, JPMorgan Chase Institute analysis shows that many people defer health spending until they get tax refunds. (Out-of-pocket health spending increased by 60% in the week after getting a tax refund.) Tax refund season is one of the few moments when families have big, lumpy sums to spend on doctors (if they don’t spend them all on filing for bankruptcy).

1. Global Development: One of the more encouraging trends in development economics as far as I'm concerned is the growth of long-term studies that report results not just once but on an on-going basis. Obviously long-term tracking like the Young Lives Project or smaller scale work like Robert Townsend's tracking of a Thai village (which continues to yield valuable insights) falls in this category, but it's now also happening with long term follow-up from experimental studies. Sometimes that takes the form of tracking down people affected by earlier studies, as Owen Ozier did with deworming in Kenya. But more often it seems, studies are maintaining contact over longer time frames. A few weeks ago I mentioned a new paper following up on Bloom et. al.'s experiment with Indian textile firms. The first paper found significant effects of management consulting in improving operations and boosting profits. The new paper sees many, but not all, of those gains persist eight years later. Another important example is the on-going follow up of the original Give Directly experiment on unconditional cash transfers. Haushofer and Shapiro have new results from a three year follow-up, finding that as above, many gains persist but not all and the comparisons unsurprisingly get a bit messier.Although it's not quite the same, I do feel like I should include some new work following up on the Targeting the Ultra Poor studies--in this case not of long-term effects but on varying the packages and comparing different approaches as directly as possible. Here's Sedlmayr, Shah and Sulaiman on a variety of cash-plus interventions in Uganda--the full package of transfers and training, only the transfers, transfers with only a light-touch training and just attempting to boost savings. They find that cash isn't always king: the full package outperforms the alternatives.

2. Our Algorithmic Overlords: If you missed it, yesterday's special edition faiV was a review of Virginia Eubanks Automating Inequality. But there's always a slew of interesting reads on these issues, contra recent editorials that no one is paying attention. Here's NYU's AINow Institute on Algorithmic Impact Assessments as a tool for providing more accountability around the use of algorithms in public agencies. While I tend to focus this section on unintended negative consequences of AI, there is another important consideration: intended negative consequences of AI. I'm not talking about SkyNet but the use of AI to conduct cyberattacks, create fraudulent voice/video, or other criminal activities. Here's a report from a group of AI think tanks including EFF and Open AI on the malicious use of artificial intelligence.

3. Interesting Tales from Economic History: I may make this a regular item as I tend to find these things quite interesting, and based on the link clicks a number of you do too. Here's some history to revise your beliefs about the Dutch Tulip craze, a story it turns out that has been too good to fact check, at least until Anne Goldgar of King's College did so. And here's work from Judy Stephenson of Oxford doing detailed work on working hours and pay for London construction workers during the 1700s. Why is this interesting? Because it's important to understand the interaction of productivity gains, the industrial revolution, wages and welfare--something that we don't know enough about but has implications as we think about the future of work, how it pays and the economic implications for different levels of skills. And in a different vein, but interesting none-the-less, here is an epic thread from Pseudoerasmus on Steven Pinker's new book nominally about the Enlightenment.

4. Household Finance: I want you to look at two pieces that are about household finance, one from the US and one Ghana and tell me if you react to them the same or differently and whether that reaction is positive or negative. I feel like these two stories are one of the most effective rohrshach tests you could imagine to get at people's feelings about financial services for poor households. First we have a blog post from CGAP about PayGo Water--in other words, rather than paying a monthly water service bill retroactively, using digital payments to enforce payment before the water is delivered. Second, this blog post from Aaron Klein about hidden price discrimination based on what payment methods consumers use--in other words the poor pay more.

5. Social Investment: Here are a few other pieces that similarly may spark conflicting responses. Ross Douthat has an editorial on the trade-offs in the behavior of corporate America as it seems to more explicitly blend socially liberal but economic-inequality-boosting policies. Fast Company reviews the state of Social Impact Bonds, a facet of social investment that seems to have fallen out of the spotlight as people realize how complicated they (and the world) are. I'm a long-term critic of the idea that social investing has "no trade-offs." If you're getting market-rate returns you're just investing as far as I'm concerned, not social investing. But this longform critique of the "doing well by doing good" rhetoric seems to me to really be talking about making grants not investments. And finally this piece doesn't truly fit here unless you really squint and cock your head to the side, but it does induce conflicting feelings. It's about continuing large-scale discrimination against borrowers of color by US banks (and in that sense it fits fairly well with the piece above), and the stories they tell will likely leave you seething. But the evidence isn't that strong since they can only see a small portion of the data you would need to really determine creditworthiness. Don't get me wrong, I'm not saying there isn't discrimination. But it seems much more likely to me that the source of the discrimination is the pre-existing racial wealth gap and biases in credit scoring, not purposeful discrimination by the banks or loan officers.

1. Algorithmic Overlords (+ Banking + Digital Finance + Global Development) book review: I'd like to call myself prescient for bringing Amar Bhide into last week's faiV headlined by questions about the value of banks. Little did I know that he would have a piece in National Affairs on the value of banks, Why We Need Traditional Banking. The reason to read the (long) piece is his perspective on the important role that efforts to reduce discrimination through standardization and anonymity played in the move to securitization. Bhide names securitization as the culprit for a number of deleterious effects on the banking system and economy overall (with specific negative consequences for small business lending). The other reason to read the piece is it is a surprisingly great complement to reading Automating Inequality, the new book from Virginia Eubanks. To cut to the chase, it's an important book that you should read if you care at all about the delivery of social services, domestically or internationally. But I think the book plays up the technology angle well beyond it's relevance, to the detriment of very important points.The subtitle of the book is "how high-tech tools profile, police and punish the poor" but the root of almost all of the examples Eubanks gives are a) simply a continuation of policies in place for the delivery of social services dating back to, well, the advent of civilization(?), and b) driven by the behaviors of the humans in the systems, not the machines. In a chapter about Indiana's attempt to automate much of its human services system, there is a particularly striking moment where a woman who has been denied services because of a technical problems with an automated document system receives a phone call from a staffer who tries very hard to convince her to drop her appeal. She doesn't, and wins her appeal in part because technology allowed her to have irrefutable proof that she had provided the documents she needed to. It's apparent throughout the story that the real problem isn't the (broken) automation, but the attitudes and political goals of human beings.The reason why I know point a) above, though, is Eubanks does such an excellent job of placing the current state in historical context. The crucial issue is how our service delivery systems "profile, police and punish" the poor. It's not clear at all how much the "high tech tools" are really making things worse. This is where Bhide's discussion is useful: a major driver toward such "automated" behaviors as using credit scores in lending was to do an end-run around the discrimination that was rampant among loan officers (and continues to this day, and not just in the US). While Eubanks does raise the question of the source of discrimination, in a chapter about Allegheny County, PA, she doesn't make a compelling case that algorithms will be worse than humans. In the discussion on this point she even subtly undermines her argument by judging the algorithm by extrapolating false report rates from a study conducted in Toronto. This is the beauty and disaster of human brains: we extrapolate all the time, and are by nature very poor judges of whether those extrapolations are valid. In Allegheny County, according to Eubanks telling, concern that case workers were biased in the removal of African-American kids from their homes was part of the motivation for adopting automation. They are not, it turns out. But there is discrimination. The source is again human beings, in this case the ones reporting incidents to social services. The high-tech is again largely irrelevant.I am particularly sensitive to these issues because I wrote a book in part about the Toyota "sudden acceleration" scare a few years ago. The basics are that the events described by people who claim "sudden acceleration" are mechanically impossible. But because there was a computer chip involved, many many people were simply unwilling to consider that the problem was the human being, not the computer. There's more than a whiff of this unjustified preference for human decision-making over computers in both Bhide's piece and Eubanks book. For instance, one of the reasons Eubanks gives for concern about automation algorithms is that they are "hard to understand." But algorithms are nothing new in the delivery of social services. Eubanks uses a paper-based algorithm in Allegheny County to try to judge risk herself--it's a very complicated and imprecise algorithm that relies on a completely unknowable human process, that necessarily varies between caseworkers and even day-to-day or hour-to-hour, to weight various factors. Every year I have to deal with social services agencies in Pennsylvania to qualify for benefits for my visually impaired son. I suspect that everyone who has done so here or any where else will attest to the fact that there clearly is some arcane process happening in the background. When that process is not documented, for instance in software code, it will necessarily be harder to understand.To draw in other examples from recent faiV coverage, consider two papers I've linked about microfinance loan officer behavior. Here, Marup Hossain finds loan officers incorporating information into their lending decisions that they are not supposed to. Here, Roy Mersland and colleagues find loan officers adjusting their internal algorithm over time. In both cases, the loan officers are, according to some criteria, making better decisions. But they are also excluding the poorest, even profiling, policing and punishing them, in ways that are very difficult to see. While I have expressed concern recently about LenddoEFL's "automated" approach to determining creditworthiness, at least if you crack open their data and code you can see how they are making decisions.None of which is to say that I don't have deep concerns about automation and our algorithmic overlords. And those concerns are in many ways reinforced and amplified by Eubanks book. While she is focused on the potential costs to the poor of automation, I see two areas that are not getting enough scrutiny.First, last week I had the chance to see one of Lant Pritchett's famous rants about the RCT movement. During the talk he characterized RCTs as "weapons against the weak." The weak aren't the ultimate recipients of services but the service delivery agencies who are not politically powerful enough to avoid scrutiny of an impact evaluation. There's a lot I don't agree with Lant on, but one area where I do heartily agree is his emphasis on building the capability of service delivery. The use of algorithms, whether paper-based or automated, can also be weapons against the weak. Here, I look to a book by Barry Schwarz, a psychologist at Swarthmore perhaps most well-known for The Paradox of Choice. But he has another excellent book, Practical Wisdom, about the erosion of opportunities for human beings to exercise judgment and develop wisdom. His book makes it clear that it is not only the poor who are increasingly policed and punished. Mandatory sentencing guidelines and mandated reporter statutes are efforts to police and punish judges and social service personnel. The big question we have to keep in view is whether automation is making outcomes better or worse. The reasoning behind much of the removal of judgment that Schwartz notes is benign: people make bad judgments; people wrongfully discriminate. When that happens there is real harm and it is not obviously bad to try to put systems in place to reduce unwitting errors and active malice. It is possible to use automation to build capability (see the history of civilization), but it is far from automatic. As I read through Eubanks book, it was clear that the automated systems were being deployed in ways that seemed likely to diminish, not build, the capability of social service agencies. Rather than pushing back against automation, the focus has to stay on how to use automation to improve outcomes and building capability.Second, Eubanks makes the excellent point that while poor families and wealthier families often need to access similar services, say addiction treatment, the poor access them through public systems that gather and increasingly use data about them in myriad ways. One's addiction treatment records can become part of criminal justice, social service eligibility, and child custody proceedings. Middle class families who access services through private providers don't have to hand over their data to the government. This is all true. But it neglects that people of all income levels are handing over huge amounts of data to private providers who increasingly stitch all of that data together with far less scrutiny than public agencies are potentially subject to. Is that really better? Would the poor be better off if their data was in the hands of private companies? It's an open question whether the average poor person or the average wealthy person in America has surrendered more personal data--I lean toward the latter simply because the wealthier you are the more likely you are to be using digital tools and services that gather (and aggregate and sell) a data trail. The key determinant of what happens next isn't, in my mind, whether the data is held by government or a private company, but who has the power to fight nefarious uses of that data. Yes, the poor are often going to have worse outcomes in these situations but it's not because of the digital poorhouse, it's because of the lack of power to fight back. But they are not powerless--Eubanks stories tend to have examples of political power reigning in the systems. As private digital surveillance expands though, the percentage of the population who can't fight back is going to grow.So back to the bottom line. You should read Automating Inequality. You will almost certainly learn a lot about the history of poverty policy in the US and what is currently happening in service delivery in the US. You will also see lots to be concerned about in the integration of technology and social services. But hopefully you'll also see that the problem is the people.

Editor's Note: I'm obviously not anti-bank (at least I hope that's obvious!), but in the wake of last week's piece on how hard it is to figure out the value of most of what banks do, I've been accumulating a number of pieces on bank behavior that are less than flattering. I've been struggling to come up with any other service that is so vital and that society so commonly holds in contempt. It's a reminder, again, of what an enormous accomplishment it was for microfinance's pioneers to get people to view banks and bankers as heroes. If there are any sociologists in the house who would like to school me on the literature of social perceptions of banking, please do!--Tim Ogden

1. Banking: In case you missed it, here's that link from last week finding that banks would be better off if they did a lot less. Well, a lot less of the complicated financial stuff that most (large) banks spend a lot of time doing. Matt Levine sees a generalized trend in a positive direction--that is that the financial engineering that financial services companies are engaged in is focused much less on engineering complex financial instruments and a lot more on software and technology engineering. Even the cool project names are being reserved for technology projects rather than hard-to-understand derivatives-of-futures-of-insurance-of-bonds-of-weather-derivatives.That does raise some questions about the evolution of fintech--if the banks themselves are more focused on the technology of service delivery, what does that mean for the technology firms? I do feel a bit of unease that these are the same banks that don't seem to be able to add value to themselves in their core area of expertise (and it's not just the banks, remember that Morningstar's ratings are negative information). How much should we expect from their wading into technology and advice? More on that below, in item 2.There's another concern with banks moving in this direction. While it's not always the case, the kind of engineering that banks are doing now tend to increase consolidation: returns to scale tend to be bigger and matter more in software, data and high-volume/low-margin activities. And when consolidation happens it tends to be bad for lower-income customers. Here's a recent paper examining the impact of bank consolidation in the US (particularly large banks acquiring small banks): higher minimum account balances and higher fees, particularly in low-income neighborhoods. Those neighborhoods see deposits flow out of bank accounts (justifying closing branches) and later see increases in check-cashing outlets and decreased resilience to financial shocks. But wait there's more: the current version of the Community Reinvestment Act regulations tend to focus on places where banks have a physical presence. So closing branches and delivering more services through technology means, well, that those banks have less worries about complying with CRA. Hey did you know that the Treasury Department is considering making changes to the CRA regulations? I'm guessing the first priority isn't going to be expanding the CRA mandates.And just to throw in a little non-US spice, here's a story about massive bank fraud at the Punjab National Bank in India.

2. Our Algorithmic Overlords: I've made jabs in the faiV pretty regularly about fintech algorithms ability to make good recommendations, particularly for lower income households. It turns out I'm not alone in distrusting machine-generated recommendations. Human beings tend to believe pretty strongly that humans make better recommendations than machines particularly when it comes to matter of taste. But we're all wrong. Here's a new paper from Kleinberg, Mullainaithan, Shah and Yeomans testing human versus machine recommendations of jokes(!). The machines do much better. Perhaps I should shift my concern away from machine-learning-driven recommendations and spend more time on a different preoccupation: why humans are so bad at making recommendations. There is perhaps another way: making humans and machines both part of the decision-making loop. A great deal of work in machine learning right now is organized around humans "teaching" a machine to make decisions, and then turning the machine loose. An alternative approach is having the "machine-in-the-loop" without ever turning it loose. That is the approach generally being used in such things as bail decisions. The big outstanding question is where we should allow humans (and which humans) to overrule machine recommendations and when we should allow the machines (and which machines) to overrule the humans.Key to making such decisions is whether the human is able to understand what the machine is doing, and whether humans should trust the machine. Both are dependent on replicability of the AI. You might think sharing data and code in AI research would be standard. But you'd be as wrong as I was about recommendations. There's a budding replication crisis in AI studies because it is so rare for papers to be accompanied by the training data (about 30%) used in machine-learning efforts, much less the source code for their algorithms (only 6%!). Of note if you click on the paper above about recommendations, on page two there is note that all of the authors' data and code are available for download.

3. Risk: Last week I promised some more thoughts on risk and aspirations. To summarize for those who haven't been following along: there is strong evidence of large returns on investment for poor farmers and even some microenterprises, there are similarly large returns for rural farmers investing in migrating to urban areas, those folks tend to avoid making such investments, and interventions that reduce risk or allow pre-commitment tend to increase such investments. More recently, several other pieces of evidence seem to be falling into place. First, those large returns on investment are not so large once you adjust them for risk (that's from the recent Townsend paper that Jonathan first linked two weeks ago). Second, urban migration might be riskier than we have appreciated. Third, people who migrate may be systematically different and more capable (and thus have less risk) than those who don't. And fourth, as I talked about last week, another way to get people to make more investment is to raise their aspirations or sense of personal efficacy, which could be interpreted as increasing their risk tolerance. There are a number of things that strike me given this set of stylized (and not yet fully proven) facts:1) There are big reasons to be concerned about general equilibrium effects of increasing the risk tolerance of people who are risk averse. It's very plausible that early experiments in this domain would show large gains for participants but those gains would not only fade, but substantially reverse at scale. If this pattern is true, it makes a very, very strong case for investing in insurance that protects people from risk rather than changing their risk tolerance.2) The pattern of risk-adjusted returns in the Townsend data looks a lot like the entrepreneurial equilibrium in developed countries, as described by Amar Bhide in his book The Origin and Evolution of New Businesses. The short version: established businesses take all of the less risky investments, leaving the truly high risk ones to entrepreneurs. Those entrepreneurs take them not only because they are less risk averse, but because they are the only options available--which is consistent with general findings that successful entrepreneurs are just as risk averse as corporate managers. But we only ever see the risks which pay off, leaving us with a profoundly distorted view of entrepreneurism.3) A book I spent some time reading while on break was James Scott's Against the Grain. One of the main claims of the book is that, contrary to the traditional narrative, the hunter-gatherer lifestyle is far less risky than the sedentary agricultural lifestyle. He makes a very convincing case using all sorts of evidence, but it raises the big question of why risk-averse agriculturalists haven't continually reverted back to hunter-gatherer lifestyles. I find the arguments there less convincing, but I'm not sure what to think about that or the implications.

4. Inequality: OK, late in the day. Time for some rapid fire links. An argument for income redistribution to address growing inequality from an unexpected source: Bain & Company. A new paper looks at whether one of the methods for income redistribution, a Universal Basic Income, discourages work by examining Alaska's citizen oil dividend and finds that it mostly doesn't, though with some effects in tradeable sectors. Overlooked in many discussions of inequality is the largest disparity in college-going: rural kids are the ones most left behind.And a lengthy piece on the hidden inequality in how people in the US make payments. The rich use credit cards and get lots of rewards (like cash back or airline miles, mostly paid for by merchants, and don't carry balances) and the poor use debit cards or cash and get nothing--making for a very regressive system. Just another way the poor are different than you and me: they pay more.

Mechanical Turk is a common source of 'warm bodies' for social scientists but it's hard to know to just who the workers who participate are, and even how many there really are--and the answers are highly dependent on how you define who is a "regular" participant. It's a complicated question to answer, and the chart below is interesting but wrong, for interesting reasons. Source: Panos Ipeirotis.

1. Digital Finance: When I name an item "digital finance" you know I'm going to be talking about mobile money and fintech--but should you? Is there something that's particularly more digital about mobile money than about payment cards or plain-old ATMs (both of which are, of course, fintech). Arguably paying a vendor with a credit card requires fewer real world actions than using mobile money--there are certainly fewer keys to be pressed. That's the overriding thought I had when looking at this new research from CGAP and FSD Kenya on digital credit in Tanzania: digital credit looks like credit cards. It's being used to fill gaps in spending, not for investment; is mostly being used by people with other alternatives; it's mostly expanding the use of credit (on the intensive margin); and it's really unclear whether it's helping or hurting.Perhaps the most striking thing is that digital credit is not being used for "emergencies." Part of the interest, I think, in mobile money and digital credit was that it might enable users to better bridge short-term liquidity gaps given the well-documented volatility of earnings. But that's not what seems to be happening. Again it seems to be mirroring other forms of digital finance that we don't really call "digital finance", namely payday loans (which after all typically involve an automated digital transfer out of the borrowers checking account). Borrowers are very likely to miss payments (1/2 of borrowers) or default (1/3 of borrowers, based on self-reports, not administrative data). Given that, these papers (one, two, three, four) on whether access to payday loans helps or hurts seem like they should be required reading for digital credit observers (and don't forget the links from Sean Higgins a few weeks ago). The gist--they do help when there really are emergencies like natural disasters, but hurt a lot when there aren't.

This week in the US is providing an unusual window into emergencies and digital finance. The sharp declines in the US stock market caused a lot of folks to go look at their portfolios, which brought down a new generation of digital finance websites like Wealthfront and Betterment. Even Fidelity and Vanguard had problems. There's an element there of concern about mobile money systems in developing countries: we really don't know what a "run" on a mobile money platform would look like and how systems and people would be able to handle outages whatever their cause. But the more important story is that the problems encountered were probably pretty good for consumers. Preventing people from accessing their accounts in the perceived emergency of stock prices dropping kept them from panic selling, which is a thing humans do a lot. In fact, for those customers that could log in, they found lots of artificial barriers to taking action. Digital finance's key contribution in this case wasn't expanding access, it was limiting it.

2. Household Finance: Which brings us back to the ever recurring theme of household finance: it's complicated and we really don't understand it very well. What we do understand is that it's very hard for people to make sound decisions (causal inference is hard!) when it comes to money. Here, at long last, is the write-up of work by Karlan, Mullainathan and Roth on debt traps for fruit vendors. You may remember this being referenced in the book Scarcity--but if not, the basics are that people in chronic debt who have their loans paid off fall quickly back into chronic debt. That also seems like something digital credit observers should be thinking about.

3. Our Algorithmic Overlords: I promised a review of Virginia Eubanks new book Automating Inequality this week, but I'm not ready yet. In the meantime, I'll point you to Matt Levine's discussion of how little of what we do matters and how big data is starting to illustrate that. It's a riff that starts from a new paper showing that what banks do doesn't seem to matter much, which I suppose is a big support to the point above about how hard household finance is--even highly paid professionals can't seem to do anything that makes a difference.

And the founder of the Electronic Frontier Foundation died this week. I found this reflection thought-provoking in a number of directions: "I knew it’s also true that a good way to invent the future is to predict it. So I predicted Utopia, hoping to give Liberty a running start before the laws of Moore and Metcalfe delivered up what Ed Snowden now correctly calls 'turn-key totalitarianism.'”

4. Aspirations, and Risk: I've been linking fairly frequently lately (e.g. this overview from David Evans or Campos et al in Togo) to work that might fall into a broad category of "boosting aspirations,” though even whether that moniker is accurate is still unclear. But there are a number of papers finding that if you help people believe that what they do matters and they can improve their lives (regardless of what the data from banks tell us), that can have a big positive effect on their behavior and outcomes. Here's a post on promising early results of another of these studies, with Jamaican entrepreneurs.

5. Surprise: I'm easing back into the faiV, and it's late in the day. So I'm going to surprise all of you and just stop there for now. But I can't not have a link, so go play this game about the "retail apocalypse" in the US that Bloomberg put together. And for the American GenXers out there, prepare for flashbacks to Kings Quest.

Editor's Note: this week’s faiV is guest-edited by none other than Jonathan Morduch. I'll be back on regular faiV duty next week. --Tim Ogden

Guest Editor Jonathan Morduch's Note: Thanks, Tim Ogden, for letting me take the wheel for this week’s faiV. Sean Higgins did a great job with last week’s faiV, and I’m feeling a bit of pressure to meet their high standards. Here we go:

1. Development Economics Superstars: You know by now that NYU economist Paul Romer is heading home to downtown NY, leaving his post as the World Bank Chief Economist. It’s good news for the NYU development economics community. Don’t worry about the World Bank, though – if this list of amazing seminar speakers is any indication, the World Bank continues to be a place to find exciting ideas and research. The first speaker was this week: MIT’s Tavneet Suri talking about digital products and economic lives in Africa (video).

2. Dueling Deatons:It would be embarrassing to let on just how much I’ve learned from reading Angus Deaton over the years. But there are different versions of Deaton. One of them is a careful analyst of income and consumption data with a no-BS attitude toward poverty numbers. Another wrote an op-ed in the New York Times last week.Deaton’s op-ed argued (1) that there’s quite a lot of extreme poverty in the US, not just in poorer countries, and (2) perhaps we should move budget from anti-poverty efforts abroad to those at home. Development economists & allied cosmopolitans rose up. Princeton ethicist Peter Singer argues that argument #2 clearly fails a cost-benefit test: it’s simply much cheaper to address needs abroad. Charles Kenny and Justin Sandefur of the Center for Global Development reject the idea that spending more in Mississippi should mean spending less in India, and they take a good whack at the US poverty data. But if you’re going to read just one rebuttal, make it Ryan Brigg’s essay in Vox. It’s the rebuttal to “provocative Deaton” that “no-BS Deaton” would have written. The main argument is: no, actually, there isn’t much “extreme poverty” in the US once you look at the data more carefully. Deaton’s basic premise thus falls away.On a somewhat more personal note: in recent years, I’ve spent time down the back roads of Mississippi with people as poor as you’ll find in the state. I’ve come to know the kinds of Mississippi towns that Kathryn Edin and Luke Shaefer describe in their powerful US book, $2 a Day (one of Deaton’s sources). At the same time, I’ve worked in villages in India and Bangladesh where many households are targeted as “ultrapoor”. So I think I have a sense of what Deaton’s getting at in a more visceral way. He’s right about the essential point: It’s hard not to be angry about our complacency about poverty – both abroad and in the US. We should be more aware (and more angry). But Deaton picked the wrong fight (and made it the wrong way) this time.

3. Risk and Return (Revisited): A big paper published this week. It’s nominally about farmers in Thailand, but it challenges common ways of understanding finance and inequality in general. The study holds important lessons but is fairly technical and not so accessible. The paper is “Risk and Return in Village Economies” by Krislert Samphantharak and Robert Townsend in the American Economic Journal: Microeconomics (ungated).Why does poverty and slow economic growth persist? A starting point is that banks and other financial institutions often don’t work well in low-income communities. One implication is that small-scale farmers and micro-enterprises can have very high returns to capital -- but (or because) they can’t get hold of enough capital to invest optimally. The entire microfinance sector was founded on that premise, and there’s plenty of (RCT) evidence to back it.Samphantharak and Townsend use 13 years’ worth of Townsend’s Thai monthly data to dig deeper. The paper gathers many insights, but here are two striking findings: The Thai households indeed have high average returns to capital but they also face much risk. Making things harder, much of that risk affects the entire village or broader economy and cannot be diversified away. As a result, much of the high return to capital is in fact a risk premium and risk-adjusted returns are far, far lower. That means that poorer households may have high returns to capital but they are not necessarily more productive than richer households (counter to the usual microfinance narrative). The action comes from the risk premium.What is happening (at least in parts of these Thai data) is that poorer farmers are engaged in more risky production modes than richer farmers. Once risk premia are netted out, the picture changes and richer farmers are in fact shown to have higher (risk-adjusted) returns.A few implications (at least in these data): (1) better-off farmers are both more productive and have more predictable incomes. So inequality in wealth is reinforced by inequality in basic economic security, the kind of argument also at the heart of the US Financial Diaries findings. (2) Poorer farmers face financial constraints, but not of the usual kind addressed by microfinance. The problems largely have to do with coping with risk. That might explain evidence that microfinance isn’t effective in the expected ways. (3) The evidence starkly contrasts with arguments made by people (like me) who argue that rural poverty is bound up with the inability to take on riskier projects.

4. Our Algorithmic Overlords: Political scientist Virginia Eubanks has a new book, Automating Inequality, [Tim will have a review in next week's faiV] about poverty in the digital age. Eubanks argues that we’re creating “digital poorhouses” akin to the poorhouses of old. The basic story is that data-driven policy approaches hurt the most disadvantaged – but seem hard to understand and thus hard to criticize. Eubanks, though, says they’re not in fact so complicated. Eubanks is featured in an interesting interview in MIT’s Technology Review. One snippet on politics and activism: “I do think it’s really interesting, the way we tend to math-wash these systems, that we have a tendency to think they're more complicated and harder to understand than they actually are. I suspect that there's a little bit of technological hocus-pocus that happens when these systems come online and people often feel like they don't understand them well enough to comment on them. But it’s just not true.” Bonus: Just learned the phrase “math-wash”.

5. Paychecks as Commitment Devices: If you’re like me, you’re probably paid monthly by your employer. A 2016 working paper by Lorenzo Casaburi and Rocco Macchiavello (which I just saw Lorenzo present – I’m very late to the party) argues that – for members of a Kenyan dairy cooperative at least – being paid monthly has an advantage that is easy to take for granted: It helps overcome saving constraints. In effect, the cooperative is saving weekly earnings so the members don’t have to. What’s most striking is that members are willing to pay (by foregoing earnings) for the chance to be paid monthly. The result lines up with other (surprising) evidence that people are willing to pay for saving mechanisms that transform small cash inflows into meaningfully large sums (to take a phrase from Stuart Rutherford).

Editor's Note: this week’s faiV highlights more research on financial inclusion and machine learning from the American Economic Association annual meetings, guest-edited by Sean Higgins, a Post-Doctoral Fellow at the Center for Effective Global Action at UC Berkeley, whose research focuses on financial inclusion.

Next week, I'm hoping Jonathan Morduch will fill in for me before I resume normal service the week of February 5th--Tim Ogden

1. Financial Inclusion: I [Sean] organized a session on savings and financial inclusion that looked at the impact of various savings interventions such as commitment devices, opt-out savings plans, and mobile money. Continuing last week’s theme on similarities between developed and developing countries, a savings intervention that has greatly increased savings in the US is opt-out savings plans or “default assignment,” such as being automatically enrolled in a 401(k) plan. In an experiment in Afghanistan, Joshua Blumenstock, Michael Callen, and Tarek Ghani explore why defaults affect behavior: some employees are defaulted into a savings program where 5% of their salaries are automatically deposited in a mobile money savings account, but they can opt out at any time. Those who were defaulted in were 40 percentage points more likely to contribute to the savings account, which is comparable to the effect of the employer matching 50% of employees’ savings contributions

Commitment savings accounts have also been tested in the US and in many othercountries. In a study by Emily Breza, Martin Kanz, and Leora Klapper, employees in Bangladesh were offered a commitment savings account, with a twist: depending on the treatment arm, employers sometimes endorsed the product, and employees were sometimes told that their decision would be disclosed to the employer. Only the treatment arm that had both employer endorsement and disclosure of the employee’s choice led to higher take-up, suggesting that workplace signaling motivated employees to save. Another study by Simone Schaner et al. (covered in last week’s faiV) offered employees in Ghana a commitment savings product with the goal of building up enough savings to stop incurring overdraft fees, which are common. Take-up was high, but baseline overdrafters were more likely to draw down their savings before the commitment period ended -- meaning they benefited less from the intervention.Two important barriers to financial inclusion in the US and around the world are transaction costs and low trust in banks. In a paper I coauthored with Pierre Bachas, Paul Gertler, and Enrique Seira, we study the impact of providing debit cards to government cash transfer recipients who were already receiving their benefits directly deposited into a bank account. Debit cards lower the indirect transaction costs -- such as time and travel costs -- of both accessing money in a bank account and monitoring the bank to build trust. Once they receive debit cards, beneficiaries check their balances frequently, and the number of checks decreases over time as their reported trust in the bank and savings increase"

2. Household Finance: Digital credit is a financial service that is rapidly spreading around the world; it uses non-traditional data (such as mobile phone data) to evaluate creditworthiness and provide instant and remote small loans, often through mobile money accounts. One of the concerns about digital credit is that customers’ credit scores can be negatively impacted, even for the failure to repay a few dollars. In turn, this can leave them financially excluded in the future. Andres Liberman, Daniel Paravisini, and Vikram Pathania find a similar result for “high-cost loans” in the UK (which we would call payday loans in the US). They use a natural experiment and compare applicants who receive loans with similar applicants who do not receive loans to study the impact of the loans on financial outcomes.For the average applicant, taking up a high-cost loan causes an immediate decrease in the credit score, and as a result the applicant has less access to credit in the future.

3. Our Algorithmic Overlords: There were a numberofsessions at the AEA meetings on big data and machine learning. My favorite of these showcased a variety of economic applications of machine learning, three of which use big data from mobile phones. Susan Athey et al. use high-frequency location data from mobile phones to estimate a consumer choice model over restaurants and travel time. There are a large number of variables going into each individual’s decision of where to go for lunch, and each individual is different; the benefit of using machine learning is that they can incorporate a large number of variables on both restaurants and consumer preferences into the model. Susan also has an excellent overview of applications of machine learning in economics here.

Mobile phone data can also be used to predict creditworthiness: in a middle-income Latin American country, Daniel Björkegren and Darrell Grissen find that mobile phone call detail records perform just as well at predicting creditworthiness as traditional credit bureau scores (although neither perform particularly well in this sample). The mobile phone data appears to be picking up useful information to predict creditworthiness, and could be especially useful for consumers with no formal credit history or traditional credit score. These data sources and models could also help low-income women, who face a bias in the amount lenders are willing to provide, higher interest rates, and legal frameworks which can make it more difficult for them to access credit.

4. More Machine Learning: After the meetings each year, the AEA offers two-day continuing education courses on a changing variety of topics. This year, one of the courses was Machine Learning and Econometrics taught by Susan Athey and Guido Imbens. The webcasts and slides from the course can be accessed here. As economics increasingly adopts methods from machine learning in the coming years, this class’s combination of practical tools, R code, intuition, and theory make it more than worth your time to watch the webcasts and peruse the course materials.

One of the gems was the intuitive descriptions of various machine learning techniques. I feel like I finally have an intuitive understanding of what stochastic gradient descent and neural nets do (and I had to explain it to a friend yesterday which is always a good test). For example, here’s Susan’s description of the “incredibly powerful” method of stochastic gradient descent (in minute 58 of this video). What we usually do: “Estimating a model is climbing a mountain. In economics the way we approached that problem historically, is if you were climbing up that mountain trying to find the parameters that maximize an objective function, at a particular point in climbing that hill there’s a gradient that tells you in which direction should I change my parameters to get up to the top of the hill and find the parameters that best fit my data. We might spend fifteen minutes of our computation computing the gradient at one point, and then climb up the hill a little bit and work really hard at computing the gradient at the next point.” The magic of stochastic gradient descent: “At each point in climbing the hill, you evaluate the gradient using just one data point from your data set…you just pick one data point and compute where you should go as if that data point was your only data point. It’s an unbiased estimate of the gradient but it’s incredibly noisy. But instead of doing 10,000 computations to figure out how to make one tiny step, instead 10,000 times you go up and down your hill, up-down-up-down, over here over there, but you’re always kind of going in the right direction. And 10,000 points later you’re almost at the top, while with our old methods you would have gone much more in the right direction but you would have just made one tiny step and you’re nowhere near the top of the mountain.”

5. Inequality: The World Wealth & Income Database group led by Thomas Piketty, Facundo Alvaredo, and Lucas Chancel at the Paris School of Economics and Emmanual Saez and Gabriel Zucman at UC Berkeley presented on global inequality and policy. Recently, the group has been combining data from household surveys, national accounts, and tax records to create more comprehensive measures of income and wealth inequality. One interesting finding they presented was that Brazil’s large reduction in inequality since 2001 -- which is based on income measured in household surveys -- goes away if we instead use a measure that combines data from household surveys, national accounts, and tax records. With the more comprehensive measure, income inequality in Brazil has been flat. They also reported that inequality is increasing in almost every region of the world, and the global top 1% have about 20% of global income. A webcast of this session is available here.

Default assignment into an opt-out automatic savings plan leads to a large increase in take-up of the savings account, comparable to the effect of a 50% savings match (from Blumenstock, Callen, and Ghani).

Editor's Note: I took some time off from my time off to attend what is officially the Annual Convention of the Allied Social Sciences Associations, but I prefer to be transparent for people outside the economics profession and just call it the American Economic Association annual meeting. Herewith are some papers I encountered in the three days of the meeting, along with related thoughts and a few other items thrown in for good measure.

Next week, Sean Higgins of CEGA will be guest editing the faiV--Tim Ogden

1. The Economics Production Function: Over the last few years, papers on microenterprises generally shared a couple of remarkable--given the general narrative--findings: microenterprises (on average) didn't grow no matter what you did to try to boost them, and women-owned microenterprises performed worse than male-owned ones. Those findings led to plenty of yowls from practitioners whose work, livelihoods and in some cases core beliefs were based on the opposite. In many conversations I had, I got the impression that people outside the profession believed that economists would publish these findings and then move on. But that perception really misunderstands the motivations of economists and the way the field works. Economists don't leave puzzles alone once they find them--the field pursues them relentlessly.The best session I attended this weekend was based on the particular puzzle of why female-owned microenterprises are less profitable. Natalia Rigol presented work following up on an earlier studies that documented the profitability gender gap, finding that the source of the gap is mostly due to lower returns from female-owned enterprises where there was another (male-owned) enterprise in the household. Those male-owned enterprises were in more profitable industries (something documented in the original studies), so the households were making quite rational decisions to allocate additional funds to the more profitable business (and making it look as if the female-owned business had 0 or negative returns). In households where there was only a female-owned business there is no gap in returns to capital. Leonardo Iacovone and David McKenzie presented on efforts in Mexico and Togo, respectively, to provide training to help women entrepreneurs improve their businesses with positive results--in both cases seemingly based on personal initiative training rather than business skills. And Gisella Nagy presented results (unfortunately there's nothing yet to point to on this one) that women tailors in Ghana show lower profitability than male tailors because there are more women tailors which drives down prices they can get in the market. This last finding is particularly important because it suggests that part of the way forward for microcredit aimed at building women's businesses is to do a much better job targeting, or as I've called it elsewhere, abandoning the vaccine (everyone gets one!) model of microcredit for an antibiotic (only people who really need it get one!) model.And all of that is just a very small sample of work being done on the puzzle of heterogeneity of returns to microenterprises and what can be done about it. I'm now sorely tempted to write an overview on all these studies, but dammit I really want to get to "subsistence retail."

2. Causal Inference is Hard: Those two topics aren't orthogonal to each other of course. One way they are joined together is my common theme about how hard causal inference is for the average person, and in particular for the subsistence (or just above) operator of a microentrprise (whether farming or retail). That's what I kept thinking about when reading this new post from David McKenzie on "Statistical Power and the Funnel of Attribution". David is writing for economists trying to write convincing papers, but this point "Failure to see impacts on your ultimate outcome need not mean the program has no effect, just that the funnel of attribution is long and narrows" is equally important for the people being treated. If the funnel of attribution is long and narrows, then its approaching impossible for the individual (not gifted with a large sample size or a deep understanding of statistics) to figure out which of their actions actually matter.There is a connection to AEA here. As I was perusing the poster displays (also known as "the saddest place on earth") I kept hearing people arguing with Jacob Cosman, the creator of a poster about how the opening of new restaurants in a neighborhood affects the behavior of existing restaurants. The answer: a very precisely estimated no effect at all. (Here's a link to an old version of the paper with somewhat different results) Economists walking by simply couldn't believe this and were constantly suggesting to the author things he must have done wrong. I was amused. My strong prior is that a person would not open one of these restaurants unless they believed that their restaurant was unique (otherwise, you would believe that your restaurant would quickly fail like the 90+% of other small restaurants and you wouldn't open in the first place). So when another new restaurant arrives, you don't actually see it as a threat that needs a response. You are, after all, different! But even if you did think you needed to respond, how would you possibly know what the right response was? Do prices matter? Menus? Advertising? Item descriptions? Coupons? The funnel of attribution on all of these is so long and imprecise we should assume that individual entrepreneurs have no idea what to do even if they wanted to do something. Which ultimately brings us back to why it's so hard to get microentrepreneurs to change their behavior in a lasting way, and why personal initiative training may work much better than business skills. Personal initiative training teaches you that what you do matters, even if you can't tell, while business skills training teaches you to do something even though you can't tell that it matters.

3. More from the Saddest Place on Earth: There's more than a whiff of desperation about the poster display area at AEA, where you often find young economists-in-training doing their best impression of a street-corner evangelist/panhandler hybrid. The possibility of being accosted by a well-meaning but overly eager job-seeker seems to keep most attendees away from the area, which is a shame because I always find some quite interesting posters. Two of note this year were about microfinance loan officer behavior. Marup Hossain looked at the behavior of BRAC loan officers after the famous (at least in these parts) TUP experiment and found that they were using TUP participants relative performance in livestock husbandry in that program to determine who to approve for microcredit loans--and that this was a good way of targeting the loans to those most likely to achieve high returns. Sarah Wolfolds had a poster on performance pay in non-profit microfinance institutions in Latin America finding MFIs making smaller loans have smaller pay-for-performance payouts but more targets--I can't find a paper behind it but I always like to highlight work looking at principal-agent issues within MFIs since I don't think that gets nearly enough attention. Other "fun" posters amidst the sadness: Declining investment in infrastructure led to rebellions against the Qing dynasty; There's a lot less excess sensitivity to income than most measures suggest; eliminating a small debt account improves cognitive function of the poor more than paying off a larger amount of debt (but not fully paying it off); and digital (non-tangible) innovations tend to contribute more to income inequality than tangible innovations.

4. Our Algorithmic Overlords: Due to a series of regrettable automotive incidents I missed several of the machine learning/AI/FinTech sessions at AEA on Friday morning that I was really looking forward to. Links to sessions here, here and here. To compensate, here are some completely different algorithmic overlords pieces to contemplate. Wired has a lengthy story about the growth of China's digital panopticon and social ratings. You should click on that and read it before coming back here to click on this link to an excerpt of Virginia Eubanks' forthcoming book Automating Inequality, so that you'll especially feel the bite of discovering how much Americans in poverty already live in an automated panopticon. I've just gotten a review copy of the book, so there will be more on this when I come back from vacation (which will be partially spent reading it).

In terms of this week's through-line, I figured I might as well get in on the Star Wars jokes that are going to plague us all, apparently, for the rest of time--Tim Ogden

1. Social Investment: Last week I was at European Microfinance Week. Video of the closing plenary I participated in is here. My contribution was mainly to repeat what seems to me a fairly obvious point but which apparently keeps slipping from view: there are always trade-offs and if social investors don't subsidize quality financial services for poor households, there will be very few quality financial services for poor households.Paul DiLeo of Grassroots Capital (who moderated the session at eMFP) pointed me to this egregious example of the ongoing attempt to fight basic logic and mathematics from the "no trade-offs" crowd. This sort of thing is particularly baffling to me because of the close connection that impact investing has to investing--a world where everything is about trade-offs: risk vs. return; sector vs. sector; company vs. company; hedge fund manager vs. hedge fund manager. The logic in this particular case, no pun intended, is that a fund to invest in tech start-ups in the US Midwest is an impact investment, even though the founder explicitly says it isn't, because it is "seeking potential return in parts of the economy neglected by biases of mainstream investors." If that's your definition of impact investing you're going to have a tough time keeping the Koch Brothers, Sam Walton and Ray Dalio out of your impact investment Hall of Fame. Sure, part of the argument is that these are investments that could create jobs in areas that haven't had a lot of quality job growth. But by that logic, mining BitCoin is a tremendous impact investment. You see, mining BitCoin and processing transactions is enormously energy intensive. And someone's got to produce that energy, and keep the grid running. Those electrical grid jobs are one of the few high paying, secure mid-skill jobs. Never mind that BitCoin mining is currently increasing its energy use every day by 450 gigawatt-hours, or Haiti's annual electricity consumption. And, y'know, reversing the trend toward more clean energy. Hey anyone remember the good old days of "BitCoin for Africa"?

3. Frustrated Employees: One of the core conceits of the microfinance movement is the idea that many (most?) poor people are frustrated entrepreneurs, with lots of ideas and opportunities available if only they had access to credit. It's one of the reasons that we didn't get the impact we were looking for from massive expansion of microcredit.The idea of frustrated entrepreneurs still lives on for a lot of the general public, but I think (hope?) it's been largely abandoned within the core of the industry. But just in case, I thought I would pass along some more evidence that the poor are frustrated employees, not frustrated entrepreneurs. Here's a paper looking at small enterprise owners in Mexico, who shrink their businesses when jobs come to town, in anticipation presumably of giving up the grind of entrepreneurship for the dream of a paycheck. And here's a look at Thai entrepreneurs operating multiple micro-enterprises that concludes that it's not lack of credit that's holding back their businesses, but their own lack of skills.One of the paradoxes of the microfinance movement was that co-existing with the idea that the poor were frustrated entrepreneurs just waiting to be unleashed was the emphasis on providing a loan with conditions that made entrepreneurial risk-taking difficult if not impossible. Field and Pande showed quite a while ago that if you relaxed the constraints on loan payment, some borrowers would make riskier investments and gain from it. Here's a recent follow-up to that work which adds further evidence--again finding that borrowers with a more flexible contract end up with higher business sales, but also that the contract does a good job of inducing self-selection of borrowers who do have more of the necessary characteristics for entrepreneurial success.It's not just people in lower income countries that are frustrated employees. Many employees are frustrated employees--frustrated that the jobs they have are terrible. Here's Zeynep Ton on the case for relieving that frustration and creating better jobs.

4. Our Algorithmic Overlords: A couple of quick hits here. First, the Illinois Department of Children and Family Services tried to use big data and algorithms to predict which children were at most risk. They're scrapping the program "because it didn't seem to be predicting much."And here's Zeynep Tufecki on the dystopia we're building "just to make people click on ads." Definitely not the impact we were looking for.

Editor's Note: Two weeks ago, I told you that the faiV would be off for two weeks, and that's technically still true, because this isn't the faiV.--Tim Ogden

1-4. An Experimental Podcast: Every month or so someone asks me if I've considered doing the faiV as a podcast. The answer is not really, because the faiV doesn't lend itself to audio at least when I'm not ranting. Also because I rarely listen to podcasts because I don't commute and realistically I'm never going to sit at my desk and listen to audio for 30 minutes or more.

But because of the Thanksgiving holiday and travel this week to European Microfinance Week I wasn't able to the faiV. So I thought it was a good time to experiment with an addendum to the faiV in podcast form. Thankfully Graham Wright of Microsave agreed to experiment with me. So we recorded a conversation about digital finance, its potential and its pitfalls, inspired by Graham's post, "Can Fintech Really Deliver On Its Promise For Financial Inclusion?"

We discuss whether mission matters, barriers to adoption, the tensions in building agent networks and why everyone who says "X is not a silver bullet" is lying. All in just over 30 minutes. Give it a listen and let me know if you'd like to hear more conversations like it.

Table of Contents:1:45 - Can Digital Finance be Transformational for the Rural Poor?

3:51 - Does it matter that most DFS providers have never had a "pro-poor" mission?

7:54 - Does the US and microfinance experience foretell the future of digital finance?

The Trolling Edition

Editor's Note: In this week's edition I'm using the world "trolling" broadly, and less negatively than it is often used. I'm using it to describe pieces that raise the ire or the fears of readers, not to suggest that the ideas are purposefully wrong or misleading. Thankfully (pun intended) I have some time to come down from the ire that writing this edition produced in me. There won't be a faiV the next two weeks because of vacation and travel, though I may produce a special edition coming out of European Microfinance Week which I'll be attending. If you're there, feel free to troll me--Tim Ogden

2. Household Finance: Before the algorithmic overlords item gets ridiculously long, let's move on to something that could fit either under algorithms, protection boards, or household finance. Entrepreneurial Finance Lab, which uses psychometrics to assess creditworthiness, has a piece on the FICO blog about how their testing for personality traits like impulsiveness and delayed gratification predicts default rates. It's such a good example of why I've been a fan of EFL while being queasy at the same time, it almost felt like I was being trolled. On the positive side it's an operationally relevant way of assessing borrowers who otherwise would be shut out of access to credit. On the queasy side, there's apparently huge variation in different cultures (while the metric remains predictive), and real questions about the immutability of the features they are testing--which cuts both ways. If they're mutable there's a question about what we are measuring; if they're immutable, what do we do about people who lost the "present bias" lottery? It's a good thing to protect people from themselves by not offering them credit they are likely to default on, but it still leaves me queasy nonetheless.In terms of others being trolled, here's a piece about Refinery29's ongoing series where women share a week-long financial diary, and then readers rip apart their life choices. I'm not entirely sure whether it's the ones sharing or the ones critiquing that are the trolls, perhaps both.And since we're on the topic of diaries and I've already gone fishing for help on one research interest of mine, here's another: I read this week that more than 100,000 puertoriquenos have migrated to Central Florida since this fall's hurricanes. Wouldn't it be great to do financial diaries of those households? It's a really unique opportunity, wouldn't be very expensive to do, and it breaks my heart to see it go to waste. If you think so too, call me.

5. Philanthropy and Social Investment: Finally, this week there were several pieces on philanthropic practice that felt like they were directly trolling me. First, Gabe Kleinman wonders "Why aren't foundation's actually helping their grantees like VCs?" Kleinman is apparently unaware this is an idea that is more than 20 years old. By the time I came into the space a little over 10 years ago, one of the first pieces I tried to commission was tentatively titled, "picking over the corpse of venture philanthropy" because it was already "old news" by then. And like Cathy O'Neil's piece, Kleinman gives lots of examples of things foundations "should be doing" where foundations are actually doing those things, he is simply unaware of them. Overall, it's a fine example of the annoyingly common, but intellectually bankrupt "non-profits should act more like businesses" idea that fails in two ways: it holds up how businesses operate in theory rather than reality; and shows a profound ignorance of the challenges of social investing. Speaking of the challenges of social investing, there is a new philanthropic effort (with big time funding including Gates) targeted at "systemic change," lamenting that it is hard to for funders to collaborate and there are few "big ideas" or non-profits able to effect systemic change. Again, grrrrrr. One of the reasons for the challenges noted is the "venture philanthropy" mindset which if taken at face value discourages collaboration and emphasizes short-term metrics. But it's also true that there have been (and continue to be) lots of collaborative efforts--these ideas are not new--and they often struggle because it's virtually impossible to arrive at agreement on how systems should change, who gets a seat at the table and what priorities should be. Co-Impact will make grants of up to $50 million for up to 4 years, which is nice, but hardly seems like the scale necessary for systemic change if those words mean what I think they mean. For comparison, the grants are at most half of 100&change, which has narrowed down to surprisingly prosaic ideas that I don't think anyone would describe as systemic.Finally, to end on a positive note, here's a piece about how the Hewlett Foundation has retooled it's grant reporting process so that's its more useful for everyone. The trolling element? I'm not a Hewlett grantee.

Duck of Minerva also known as Steve Saideman writes about the peer review process and who is doing the work in various social sciences. It's a whole new form of inequality!

The Conundrum Synthesis Edition

Editor's Note: This week I attended a 2-day AspenEPIC meeting on consumer debt (in the US) and then a day with the Filene Institute on the "Mind-Money Connection." This week's title is inspired by some of Ray Boshara's comments at EPIC about conundrums in understanding consumer debt. But both events once again illustrated the desperate need for more synthesis of ideas and experience between the developing world and the developed world on financial inclusion. Ray also pointed out to me that while I introduced myself when I took over writing the faiV nearly 2 years ago, it's not apparent on a regular basis who the "I" is. So from now on, I'm going to sign these notes each week--Tim Ogden

1. Appropriate Frictions and End-User Behavior: A key theme of the EPIC conversations on debt from my perspective was the importance of differential frictions in access to various kinds of debt. One example: it's much more time consuming to open a home equity line of credit than a credit card account. There are reasons for that of course: we want people to be careful about borrowing against their home, because we fear the consequences for people if they default. But the cost of unsecured credit is so much higher, and various forms of debt are so interlinked, that households can end up in worse straits precisely because we tried to protect them. The true conundrum of appropriate frictions is that the process of determining the best form of credit for a household is in itself a friction that drives consumers toward those willing to provide credit without a care for its impact on the household--a somewhat obtuse but accurate way of describing predatory lenders.This is one of the lessons from microcredit. Demand for microcredit in most contexts is actually quite low, and rarely did microcredit have much of an impact on local moneylenders. The reason of course being that taking a microloan usually involves a lot of friction, while borrowing from a moneylender is low friction. Those operating in the US will immediately see the exact overlap with payday/auto-title lending vs. working with a community development credit union.But it's not just a question of the behavior of consumers. Front-line staff also play a role; they are an under-recognized form of end-user that has to be taken into account. Here's some new work by Beisland, D'Espallier and Mersland on "personal mission drift" among credit officers of Ecuadorian MFIs. Now don't look away because this is about microcredit or Ecuador--it's directly applicable to any kind of financial service offered to any kind of customer anywhere. Beisland et al. find that as credit officers gain experience they tend to serve fewer "vulnerable" clients (e.g. smaller loans, young borrowers, disabled borrowers). Why? Because it takes too much time--there are those frictions again. Figuring out how to offer quality products, especially credit, with appropriate frictions for both the borrowers and the credit officer, is a conundrum everywhere.For further evidence of this, check out the similarities between this piece from Bindu Ananth about conversations with newly banked customers in Indian cities, and this report on "Generational Money Chatter" in the US from Hope Schau and Ignacio Luri (especially from GenXers and Millennials). The common theme I perceive: lots of questions and uncertainties about products and providers, little faith in the "systems," and confusion about where to turn for trustworthy advice.

2. Frictions, Temptation and Digital Finance: Those of you working in the digital finance world may already be thinking about how digital tools can lower frictions--after all, not only can FinTech tools more quickly and easily gather data from consumers, but they often cut the front-line staff right out of the equation! Take that, friction!Oh but friction can be useful. This is one of those areas where I'm constantly baffled at the disconnect between the developed and developing worlds. In the developed world, it's generally understood that the goal of payment and digital finance innovation is usually to remove friction specifically for the purpose of getting people to spend more money, more often. Amazon didn't develop and patent one-click ordering out of concern for saving people time (Interesting side note, Amazon's patent on one-click expired last month--exogenous variation klaxon!). The sales pitch that credit card issuers make to merchants has always been that credit cards induce people to spend more.Here's one of my favorite new pieces of research in a long time: a study of how people in debt management plans handled spending temptation (if that description is too dry to get you to click, try this one: "Target is the Devil!"). The sub-text, and sometimes text, is how hard retailers and some credit providers work to break down the frictions that prevent people from spending.What's the connection to digital finance, particularly in developing countries. I'll enter there through this piece from Graham Wright based on a debate at the recent MasterCard Foundation Symposium on Financial Inclusion. Graham was asked to make the argument against the hope for digital finance serving poor customers. His list of five reasons why digital finance is "largely irrelevant" in the typical rural village is worth reading at face value. But it's also worth thinking about in terms of how much of digital finance is aimed at removing frictions, how it's failed to remove some of those frictions for poorer customers and what can (or will) happen to poor households when appropriate frictions are removed.

3. Quality Jobs: Another conundrum that deserves more global synthesis is the struggle to create quality jobs for low-income households. Certainly one factor of quality jobs is how much they pay. While there's little doubt that productivity has a big role to play in wages, it's not always clear, particularly since 1973 (the year I was born, coincidence?) how close that link is. Stansbury and Summers have a draft of a paper arguing that the link is still pretty strong. Josh Bivens and Larry Mishel push back, arguing that policies that undermined workers' power led to a divergence of wage growth and productivity growth, and a continuing decline in jobs that pay well enough to be quality jobs.The stagnant wages for many workers since the 1970s is one of the reasons it's clear to me that it is no longer sufficient to look at having a job as binary. Here's a new review on jobs and recidivism that finds evidence that the quality of job is what matters in helping the formerly incarcerated stay out of prison. Here's a paper from Haltiwanger, Hyatt and McEntarfer on another aspect of job quality--a chance to move up the "job ladder"--and who is getting the opportunity to move up. The surprising news: less-educated workers are more likely to move from a low-productivity firm to a high-productivity one (which should lead to higher wages, but per Mishel et al above, perhaps not).There's more than one way to take on raising the quality of jobs. Here's work from Akram, Chowdhury and Mobarak on the effect of people moving out of poor areas for better jobs. In short, they are studying a program that subsidizes rural Bangladeshi villagers migrating to cities during the low agricultural season. We already know that raises the wages of the migrants, but it also helps those who don't migrate by tightening the labor market in the villages. Here's where I have to mention that geographic mobility in the US is declining. Meanwhile, take the example of Chicago where segregation continues to shut people out of access to quality jobs, and more. Time for more programs to help Chicagoans migrate, I think, perhaps by reducing some of the frictions.

In case you were too lazy to click on the link to David Evans' mapping of 2017 NEUDC papers, here's the first chart. To get the other two, you're going to have to click. And scroll. Via Development Impact.

1. The Future of Microfinance: A few weeks ago I linked to a curious piece about the future of Indian microfinance, that seemed to be praising the swallowing of MFIs by traditional banks and justifying the extinction of MFIs that tried to go it alone. Dan Rozas wrote to point out some of the subtext: The pattern in India is similar to other countries, with the largest MFIs turning into banks. And while there are mergers and acquisitions, it is still largely the same organizations serving poor customers, "only now they're called banks." This week Barbara Magnoni tweeted from the Foromic conference that "microfinance is stale" so I asked her what she thought was next. Her response: "[B]ig MFIs win, digitize processes, poor too expensive to reach. Poor go back to cash/informal markets/ and consumer loans.YAY?:("Between the two comments, I feel like the future of microfinance is already here, right here in the USA! Per Dan's note, the transition in India and elsewhere sounds a lot like the history of banking in the United States, right up through credit unions. And per Barbara's note, the next step is pulling back from poorer customers because they are more expensive to serve. So you end up with a system where even an institutional form whose original reason for being was to serve the excluded and put "clients at the center" (to borrow a phrase) has, aside from exceptional organizations, left the poorest behind. The global microfinance movement, I think, needs to spend a lot more time looking at the financial services landscape in the United States, because that is where, absent some major investment, are headed: nearly ubiquitous financial services, but very little quality available to lower-income customers, with plenty of predatory or just indifferent-to-the-effects-on-poor-customers actors ready to fill the gaps. I guess you could say that's the negative way of making the "Case for Social Investment in Microcredit".To keep things from going too dark right off the bat, here's the story of how BRAC's MFIs in Liberia and Sierra Leone managed the Ebola crisis and it's aftermath (blog summary). And a shout-out to the Global Delivery Initiative for writing up stories like this in sufficient detail to be operationally useful. OK, that's enough optimism for me. You might be skeptical of my take on where microfinance is headed. So let me present this piece from Matthew Soursourian over at CGAP on what can happen when we push consumers toward digital merchant payments, drawing parallels to the US experience.

2. The Future of Digital Finance (and of us all): That last piece could just as easily have fit here, so to encourage you to read it, let me just say again: the future of digital finance is already here, and contrary to popular opinion, it's in operation in the United States. Still not buying it? Here's another CGAP piece drawing on the US experience: "How Developing Countries Can Prevent Their Own Equifax Breach." The encouraging thing is that this possibility is being considered; the discouraging thing is that David Medine is probably wrong: developing countries can't prevent their own breaches. At least there is no evidence so far that institutions are learning from examples like this, given how pedestrian the causes of such breaches are.At a more macro level, Tyler Cowen and Matt Levine (a dreamteam if there ever was one) discuss where technology is taking finance. Pay special attention to the section in the middle where they discuss whether technology ultimately increases or decreases access to credit. While they don't mention it specifically, this is the big question mark about Lenddo and EFL, and their like, that I mentioned: while the algorithms will rescue lots of people who are good credit risks but can't prove it conventionally, they will also likely simultaneously lock bad credit risks out of the system permanently.Speaking of being locked out, here are Charles Kenny and Cordelia Kenny reporting on a forum at CGD on women being locked out of FinTech companies and how that affects what services are being developed and who is served.

3. The Future of Our Algorithmic Overlords: One domain where the US is not the future is massive state infrastructure for identification and tracking. There the future is India (and China, but today we're talking about India after talking about China for the last several weeks). The best case scenario is that Aadhaar becomes a "societal platform" for delivering services much more efficiently and effectively. And there's evidence that it does. The worst case scenario is that Aadhaar is an insecure store of personal data that the state can nevertheless (or perhaps especially) use as a tool of control and coercion. And there's evidence of that too.

4. The Future of Household Finance: OK, this may be the only single link entry in the faiV in months, but I can't let this opportunity to bang on one of my pet drums pass by. In this edition of Matt Levine's newsletter he talks about evidence on the value of Morningstar's ratings of mutual funds (as usual you have to scroll way down to get to this section). Aside from a useful discussion of confounders in the data, he points out that Morningstar's ratings are worse predictors of future performance than some of the individual data points that make up the rating. In other words, Morningstar's ratings are negative information.What does this have to do with household finance? Well, we're in a world where we broadly expect households to be able to compare financial services and make good choices about optimal products. But even the experts in stable industries with decades of detailed data produce ratings that are worse than useless. We should expect the future of household finance is lots of services to "help" households make financial decisions using algorithms and data, and that those ratings are going to be wrong.

1. The Search for Truth, Part II: Last week's opening theme was about how hard social science is. I often find there's an unspoken wistfulness in social science research for the clear questions and clear answers of the "hard sciences."But cheer up! It's just as bad on the other side of the fence. When you're frustrated that there doesn't seem to be a biological mechanism that explains the long-term positive outcomes of deworming, remember that we have no idea--literally, no idea--what causes "side stitch," that shooting pain we've all had in our abdomen during exercise. And when you're down in the dumps that so many development interventions don't seem to show much effect, remember that the universe shouldn't exist, and we don't know why it didn't explode nanoseconds after coming into being.On the other hand, Ioannidis, Stanley and Doucouliagos' paper on how vastly underpowered most economics papers are has finally been published (it's been circulating for awhile). If that's not enough to send you back into despair, the fact that economists need to be reminded of basic good practice in presenting their ideas--per this slide deck from Rachael Meager--might do the trick. Don't get me wrong, it's good advice. But I was reminded of the time I attended a conference for PR "professionals" where the advice included such gems as, "Make sure the reporter you're pitching actually covers the topic" and "Read the last few articles the reporter wrote." Last year I was joking with Jessica Goldberg about starting a side-business editing the introductions and slide decks of job market papers. Perhaps I shouldn't have been joking.

2. The Mess that is US Higher Education (or Labor Markets are Broken All Over): Studying labor market inefficiencies is a common topic in development economics (yes, this is clickbait for David McKenzie). But as in so many domains, the problems we study in developing economies also exist in developed ones, just wearing a Halloween mask. Here's a new study on "credentialism" in the US labor market, the demand for college degrees for jobs that have no reason to require a college degree (as demonstrated by the fact that the vast majority of people currently in those jobs don't have one). That's bad for employers who pay some of the cost of the self-imposed mismatch in the labor market, but it's much, much worse for potential employees who are shut out of well-paying, stable jobs for no good reason. Unless, of course, they spend large amounts of money to get a credential. The large, and growing, lifetime earnings gap between those with a credential and those without has justified the incredible growth in student debt to finance these credentials. But if the credential is just an artifact of herd behavior among employers...And why are those credentials so expensive? One reason is that the universities providing those credentials are spending, and borrowing, huge amounts themselves in order to attract the students who have to get the credential to apply for a job. So the students borrow, and borrow some more. And then they get shut out of programs for loan forgiveness that they are should be eligible for, because the system is a mess. But don't worry, if their debt gets too out of hand, they can discharge those loans in bankruptcy. Oh wait, we changed the bankruptcy law so they can't ever discharge those loans. Don't forget too that large numbers of the people we've pushed into needing a credential are entering universities, taking loans, but never getting the credential (e.g. 70% of single mothers who enroll).And the advanced degree market may be worse. A few weeks ago I featured some work on English football academies juxtaposed with a paper about the Clark Medal. Perhaps my comparison was too oblique--so here's a piece from Nature making the connection explicit. The chances that a Ph.D. student will land a permanent academic job in the US or UK is well under 10%. The reason it's plausible to offer job market paper editorial consulting is that the premium for a well-written paper is so large. And it's large because there is massive over-supply.For those newly minted Ph.D.'s taking adjunct teaching jobs just so they can stay marginally attached to academia and perhaps make enough to supplement their food stamps, I have bad news. Current students (bachelor's and master's students that is) teach just as well as adjuncts, suggesting that "student instructors can serve as an effective tool for universities to reduce their costs." Oh right, I was trying to avoid a novella.

1. The Search for Truth: The New York Times Magazine has a long piece about Amy Cuddy, the social psychologist of "power posing" fame, and the messy process by which her research has been popularized and then discredited. The piece suggests that Cuddy (though it by no means holds her out as blameless) has been uniquely and personally targeted as the face of unreplicable and bad social science in an era of changing research practices and expectations, perhaps because she is a woman. More broadly it ponders whether the process and social conventions of communication around challenging social science research may do more harm than good. It points specifically to Uri Simonsohn, Joseph Simmons and Andrew Gelman and their roles in both calling out bad social science and in specifically highlighting Cuddy's power posing paper as an example.It's well worth the long read, careful consideration but also some critical evaluation. The piece comes at a very interesting time, with the Weinstein saga, #MeToo, and more specifically the push back about Econ Job Market Rumors and bad behavior in economics. It's important to read the piece in the context of such things as EJMR and this anecdote from Rohini Pande (in an interview with David McKenzie this week) relating how a "senior male World Bank economist wrote to our senior male colleagues at MIT and Yale asking that they review our work and correct our mistakes" in one of her early papers (with Esther Duflo; see question 4 in the link, but read the whole thing, it's very good on a lot of topics).But on reflection, I don't think the idea that Cuddy was uniquely targeted or treated more harshly than others holds water. It only appears so to a New York Times reporter because Cuddy's works is the kind that gets broad attention. Remember when Ben Goldacre kicked off "Worm Wars" with an amazingly condescending piece asking people not to point and laugh at Miguel and Kremer for the supposed "errors" in their Worms paper because they shared their data? Or the language and dudgeon around Reinhart and Rogoff's Excel error? Or the intemperate words flowing around the failure to replicate John Bargh's priming work? From another field, here's some pointed language challenging a recent result on gene editing alleging some pretty basic errors. Of course, the commonality of bad behavior in academic circles doesn't excuse it. But that cuts both ways. Cuddy has also been using this faulty logic in her own defense. As far as I can tell, her main defense has always been "everyone was engaging in bad research practices, so it's not my fault", and that's definitely the implication that the NYT article gives. I don't see much distance between that and people excusing sexual harassment because they were "raised in the '60s and '70s."Could the practice of social science be better? There's no question, but it's also not clear exactly how, other than the obvious avoidance of misogyny, ad hominem and personal attacks. But that line is difficult to see sometimes because the nature of social science research requires a great deal of personal investment. It's hard not to feel attacked when one's research, quite literally one's life's work, is criticized.To me, the most thought-provoking part of the NYT piece is when Simmons, reviewing an email he sent to Cuddy about follow-up work on whether the power posing research was reliable, says "that email was too polite" given how serious he thought the problems were. And there is a lot of bad science that needs to be called out. This week, there's yet another update to the Brian Wansink saga--several papers flat out misrepresent who the study participants were (e.g. a paper claiming participants were 8-11 when they were 4-5). Not calling bad science out, I think, is a real contributor to real world problems, like Chief Justice John Roberts being able to call good political science research "sociological gobbledygook."Here's a Chris Blattman thread on his reactions. Here's Andrew Gelman's response to the NYT piece and for the sake of this topic it is one of the few posts anywhere on the internet where you should read the comments. Someone in one of the Twitter threads wondered about the responsibility of Gelman and other bloggers like Tyler Cowen to police their comments. I'm sympathetic to this idea, but I'm old enough to remember policing comments on my own blog. It's an incredibly time-consuming and soul sucking affair with lots of trade-offs. The "business model" of blogging just doesn't allow it. In fact, in some ways it was the business model required to police commentary, also known as paid journalism, that led to blogging: the gatekeepers of commentary shut out too many voices who should be heard. Science, and the pursuit of truth, is hard.

4. Digital Finance: Now let's tie these last two themes together. China is not only building a panopticon in Xinjiang, it's also ramping up it's efforts to track deadbeat borrowers with a national database and public shaming. I'm sure that's going to go well.In other credit access news, I've long been a champion of Entrepreneurial Finance Lab, which uses psychometrics to assess credit-worthiness of small business owners, allowing more of them to get access to credit. At the same time, I've long been very wary of Lenddo, which uses alternative data, like social media connections, to assess credit-worthiness of individual borrowers. I've called it, I believe, "a tax on poor people's family ties." I've been able to avoid the cognitive dissonance of these two perspectives until this week. Lenddo and EFL announced that they are merging. Now I really don't know how to feel.Finally, here's a story about the Gates Foundation funding fintech infrastructure software for interoperability of mobile money platforms? The one thing that's clear here is that the reporter doesn't understand the topic.

The Weah-dition

Editor's Note: This is one of those weeks that you get to learn a little more about me. The "Weah-dition" is a way of bringing together the sound that I, and many other of that rare species of die-hard American men's national soccer team fans, made as the US men were eliminated from the World Cup; the fact that famous footballer George Weah is leading the Liberian presidential elections; and to tie into one of our common themes on migration and labor mobility, that George Weah's son, Timothy, is currently in India starring for the US U-17 men's national team (while under contract for Paris St. Germain). You'll see these themes return in the items below.

1. Evidence-Based Policy: Yesterday I was at a workshop hosted at Yale SOM and funded by the Hewlett Foundation on how to better connect evidence to policy. The workshop was part of a bigger project and a series of reports are coming that I will share when they are available. There was a lot of good discussion, but I thought I would share two thoughts that I find to be missing appropriate weight in evidence-based policy discussions. First, there is often discussion of a mismatch in the time horizons of researchers, implementers and policy makers. While this is no doubt true, the mismatch between those groups is trivial in comparison to the mismatch all those groups have with the amount of time it takes for change that people can feel to occur. Deworming's important effects--on earnings, not school attendance--are only felt decades after treatment. Moving to Opportunity similarly has a decade-scale effect. Few if any of the researchers, implementers or policymakers are still going to be around when the world really is undeniably different because of them.Which brings me to the second point. The enterprise of evidence-based policy is grounded in marginal improvements across large groups of people--and that's a good thing! I'm a big believer in the value of marginal improvements (QED). But people have a really, really hard time noticing or caring about marginal improvements. Human beings prefer stories about big changes for a few people with unclear causality a lot more than they do about marginal gains with sound causal inference. I'm more and more convinced (becauseofevidence!) that hope is a key ingredient for even marginal impact, but hope comes from Queen of Katwe, not from 1/10 a standard deviation improvement in average test scores. So the unanswered question for me in this conversation is, "How do we manage the tension between the policies that are good for people and the policies that people want?"In other evidence-based policy news, here's a rumination on the difficulty of applying research to practice in democratization (specifically Myanmar). And here's Andrew Gelman on not waiting for peer review, particularly in Economics, to start putting evidence into practice.

2. Evidence-Based Operations: OK, so there's one more thought: the gap between policy and research, and operations. But rather than a long discussion on that topic, here's a very good new piece on the operational choices of front-line social workers and the gap between policy (whether evidence-based or not) and practice. The challenge in the spotlight is not the Marxist-style view of workers dissociated from their work by rules but workers dissociated because of having too many morally-fraught choices. More light-heartedly, here's a piece that illustrates how hard it is to go from evidence to operational choices, as reflected through the failure of the US men's soccer team (I told you it would return). There is growing attention to front-line staff and the "product" as actually experienced by the beneficiary in impact evaluations, but much more is needed as far as I'm concerned.

3. Our Algorithmic Overlords: Speaking of operations, one of the areas where more attention is needed is the way that operations are being instantiated into algorithms that are opaque or entirely invisible. Ruben Mancha and Haslina Ali argue that that the unexamined algorithm is not worth using. Of course, they are arguing from ethics, not from business profits, where it's abundantly clear that unexamined algorithms are worth using.Here's a piece about technology-related predictions from Gartner, a tech industry research and advisory company. Skip the first three to see some striking predictions about AI-generated false information, such as that people in "mature economies will consume more false information than true information." There's a threat to advancing evidence-based policy that definitely wasn't on the agenda yesterday. I started my career at Gartner way back in 1995 and I remember one of the first things we were given to read was an an article in Scientific American about the coming age of fake photography and video. Apparently that future has finally arrived.

5. Migration: In an attempt to wrap everything up, here's a piece about Syrian refugees in Turkey, which paints a remarkably positive picture of business creation, job creation and economic growth. The Turkish economy and Turkish workers seem to be seeing substantial gains both in the short- and long-term from the presence of Syrian refugees. In fact, you could argue that changing the way the pie is split between the Syrians and Turks in terms of access to safety (and access to safety, by the way, is one of the things that the upper middle class in America is hoarding) is growing the pie for everyone. It's also similar to the benefits that George Weah's ability to flee violence in Liberia is benefiting America's soccer team and PSG over the long-term. Of course, most of the polity in both cases is focused on the short-term costs rather than the longer-term average marginal gains.

Returning to the theme of inequality, the Urban Institute has updated it's 9 charts about wealth inequality in America.

Editor's Note: I briefly had ambitions of making this week's faiV a real departure in terms of content and sources. But there's just too much good stuff in the faiV wheelhouse, so instead of "Now for Something Completely Different," we have some "Some Completely Different Things." I would still advise you to beware of Hell's Grannies, know what I mean? Still, I'm going to try to make this faiV different by being uncharacteristically laconic (except for the first item).

1. Abusive Practices: This is the part of the faiV that is different. But, perhaps contrary to the evidence, I have to hang onto the belief that making abusive practices in many domains more visible will in fact play a role in changing those practices. So first up is a piece about abuse of the elderly in Nevada where for years shady operators, aided and abetted by courts, legislators, medical professionals and other nominally civil servants have cooperated to revoke the rights and steal the assets of vulnerable people. That may seem an abstruse topic, but I think it has lots of parallels in many domains. Often, abuse of the vulnerable is tied to weak institutions or institutions that have no duty to those abused. Here we have strong institutions in many cases explicitly designed and supposedly devoted to protecting the vulnerable, which were turned against the people they were supposed to protect and which made challenging what was going on virtually impossible. As an aside, I have to commend Beth Rhyne of CFI who began talking years ago about the challenges that an aging global population would bring to financial inclusion and protection efforts.At the other end of the age spectrum, here's a piece about the "1% winners/99% losers" labor market of young football/soccer players in England. It's a form of vocational school that consistently lies to 10 year-olds and their families and then dumps the vast majority of them at age 16.Stretching even further afield, I'm hoping that many folks will find the time to read, or at least scan, the NY Times article on Harvey Weinstein, the movie mogul, and his decades of sexual harrassment and abuse and cover-ups. I'm particularly struck, if not surprised, because Weinstein moved in supposedly progressive circles. His behavior was apparently an open secret but did not dissuade many from working with him and for him and apparently participating in the glossing over of the abusive practices that let him continue. This piece about the lack of criticism coming from Hollywood is particularly pointed.And now to connect this back to something more faiV-like: I hope the Weinstein saga provides further momentum behind efforts to reform practices and behavior in the social sciences, particularly when it comes to the academic job market. There is a rapidly growing effort particularly in Economics (with offshoots as far as I can tell in Political Science and Sociology) to make job market information more transparent, but more importantly move it away from sites like EconJobRumors which facilitate abusive behavior. Check out the hashtag #EJMinfo for more. This is a rare obvious opportunity to choose between the type of behavior that enabled Weinstein, and the type of behavior that will make such abusive practices and behavior impossible.

2. Economic History, History of Economics, and Evidence: Pseuderasmus, the pseudonym for an economic historian whose real name I don't know, has a long (long, long) post about the productivity gap that opened between India and Japan in the first 30 years of the 1900s. It's filled with fascinating historical details, so even a skim of it will be rewarding. The short version is that the power of unions in Japan was restrained by demographics, culture and the government which allowed manufacturers there to innovate far more quickly and increase productivity. This in turn left Japanese workers eventually far better off than Indian workers where labor unions exerted more power.Beatrice Cherrier and Andrej Svorencik have a new paper examining the history and evolution of the Clark Medal and it's winners. Again there are plenty of interesting details to reward even a skim. I took particular note of the concentration of winners--eight universities account for all winners in terms of where their degree was earned, and 10 for all winners in terms of employment when they won. So economists are apparently uniquely good at identifying talent early, right? Right?Finally, this week I stumbled on a newish site, Straight Talk on Evidence, that reviews not the evidence for various programs and policies (for instance as the Cochrane Collaboration, AidGrade or 3ie does), but the claims made by studies that are part of the evidence base. It's a project of the Laura and John Arnold Foundation. Here is their review of an evaluation of CCTs in the United States and of a Heckman paper on the long-run health impact of early childhood education.

3. Microfinance: Last week I wrote about the differences between US and global microfinance conversations that I am exposed to. This week there's a story from the Economic Times that illustrates more confluence than divergence, speculating that MFIs in India are struggling for survival, particularly in the face of increased competition from institutions that are banks or are at least more like banks than traditional MFIs. It feels like a Straussian reading is necessary to really understand what point is being made, but I'm not steeped enough in the politics of Indian microfinance to feel confident I'm correctly interpreting the only-semi-hidden advocacy in piece. Surely the writer doesn't actually believe that traditional banks are going to fall over themselves to provide quality services to the poorer sections of Indian society?

Caitlin Tulloch is frustrated that two different cash efficiency metrics are being used in papers/reports and they mean different things. She drew up this helpful explainer. For my part, I think we should all uniformly adopt the Cost-Transfer Ratio. Souce: Cailtlin Tulloch, used by permission.

1. Basic Income: I haven't touched on basic income in what seems like months, but that's because there was little to report. This week Planet Money has an episode (adapted from 99% Invisible) on the details of what basic income is and how it might be delivered. And apparently last week, Y Combinator announced some more details of their US Basic Income study. If details matter to you, you'll be pleased to know that the work in Oakland that received a lot of attention last year was a feasibility study and now they are planning an RCT with 3000 individuals in two different states.

2. Methods and More: My next book of interviews is about big data and machine learning (If you have a better name than "Dated Conversations," let me know). Susan Athey is the first person I interviewed for the new book this past spring (I hope to have some excerpts of that interview available soon) in part because of some things Athey had written on how machine learning will change the field of economics. There's a new version of a (preliminary) paper on the topic. It has details.More specifically on details and methods, here's a new paper on the use of randomization to study network effects, a quite tricky prospect. But when it comes to methods and details mattering, two items this week really hit the nail on the head. First, Buzzfeed of all places has a lengthy piece examining the myriad problems that have emerged as people examine the details of studies published by Brian Wansink's Food and Brand Lab at Cornell. Missing data, mis-described studies, statistical errors, it's stunning. This week also saw publication of what is many ways the exact opposite of what appears to be have happened at the Food and Brand Lab: David Roodman's incredibly detailed review and replication of the research on the relationship between incarceration (or decarceration) and crime rates for the Open Philanthropy Project. The starkest contrast for me isn't actually the attention to detail but the philosophy. The Wansink saga began with a blog post that indicated that the Lab was torturing data until it said what they wanted; the Roodman review and replication was done because they were concerned that their beliefs were wrong.

3. Microfinance, US and Global: My expertise and knowledge is definitely concentrated in global microfinance rather than microfinance in the US, but because of the work on the US Financial Diaries I'm learning a lot more about the US. This week for instance I got to hang around the outskirts of the Opportunity Finance Network meeting. There are no links here but a couple of things have really struck me and so I wanted to note them, and invite you to tell me what you think/have seen, etc.First, I was really surprised about how open the US microfinance community is about the presence of and need for subsidy. Globally I see an almost totemic adherence to the idea of self-sustainability, even in the presence of compelling evidence of the prevalence of subsidy. I'm sure that's a consequence of how those industries have evolved but I'm curious about any ideas about the details of the US microfinance history that led to this.Second, two parallel conversations really struck me. One was about "community investment" in order to create "quality jobs." The second was about how to use technology to cut down costs of making loans, costs that are mostly about staffing--or in other words, how to expand microfinance by lowering the need for quality employees in the lenders. I bring this up not to point fingers about hypocrisy, but to raise the inevitable trade-offs for MFIs everywhere about reach and cost. The tension doesn't seem to exactly be on the surface in the US but it is more apparent than in global conversations, where the value of the jobs created by the global microfinance movement seem to be ignored, especially in the rush to digital finance services.Finally, I was quite surprised at the contrast in attention to borrower outcomes. Again, I'm a novice here, but whereas in international conversations I feel that everyone is talking about "impact" in terms of household incomes and consumption, in the US conversations I've been a part of, the focus seems to be much more operational--in other words, does the business continue to exist, repay and take another loan. That may be a consequence of starting from a more "antibiotic" theory of change and serving existing businesses with documented troubles accessing capital, but again I'm interested in any other perspectives.

4. US Inequality: The release of the Republican "tax plan" this week was the inspiration for the title of this week's edition but since there really is nothing there I'm not going to link to it. My go-to for keeping track of the details and what effect they will have is Lily Batchelder (NYU Law and former tax counsel for the Senate Finance Committee). Just scroll through her Twitter timeline to understand which details matter and how much.If you are interested in details of Americans' financial situation, there are two notable reports this week. The Consumer Financial Protection Board published the first "Financial Well-Being in America" report. There's lots to digest but a broad summary might be: there are big racial and gender gaps in financial well-being, but also big gaps within groups so that no particular feature is a reliable predictor of well-being. The Fed released the 2016 Survey of Consumer Finances this week as well. Details galore, but here and here are some overviews which boil down to: "the biggest gains continue to flow to the richest Americans." And here is a bizarre misreading of the details from the Washington Post. It's as if no one had ever said "correlation is not causation."Finally, here is a heartbreaking story about how the poisoning of Flint, MI's water system led to a huge spike in fetal deaths. Of course, the story is by the same person who did the "if you want to be wealthy, buy a house" story.

5. Education: Finally, in detailed reports released this week, the 2018 World Development Report (yes, the same week as the 2016 SCF, the actual year of publication is apparently a detail that doesn't matter) is out with a focus on education, particularly on the need to focus on learning rather than measures of schooling. Make sure to congratulate David Evans. My favorite take on the new WDR is from Justin Sandefur, who in this tweet stream points out that "all sides seem to embrace the learning crisis and still find justification for their previously chosen policies" (with linked examples). You'll have to check the details to see if you agree.

Frederica Frangapane and Alex Piacentini have created a site to visualize the stories of 6 migrants who arrived in Italy from four different countries in 2016, called The Stories Behind a Line.

The New and the Old Edition

Editor's Note: Most of the items this week are in some way new additions to items that have been featured in the faiV the last few months, or at least updates on some long-running themes.

1. Microenterprise and Household Finance: I assume that most of you are familiar with David McKenzie's business plan competition in Nigeria (there's even a Planet Money episode about it!) and his cash drop work (I have to use this self-serving link of course). David and co-authors have a new paper in Science (summary/blog version here) testing the effectiveness of business training for microenterprises in Togo and find that a standard business curricula did not do much (in line with lots of other business training studies, though most are plagued by too little power) but a curriculum based on boosting personal initiative did have large effects.I see this as lining up with a stream of research finding that boosting aspirations or "hope" can have meaningful impact in many different contexts (see for instance, this recent work on effects of watching Queen of Katwe) and through a variety of interventions (any one know of an overview of recent work in this vein?). It also helps explain why there seem to be only small effects of business training on businesses that objectively should have lots of gains from marginal improvements in operations--if you don't believe that running your microenterprise better will matter...In other microenterprise/microcredit news, I learned this week about a study (new draft coming soon apparently) that tests allocating microcredit based on peer views of microenterprise owner business skills. Those ranked highly do in fact see large returns to a $100 cash drop (8.8 to 13% monthly returns). I heard about the study from this excellent thread from Dina Pomeranz on a talk by Abhijit Banerjee and Esther Duflo on what new they've learned since that "old" book Poor Economics came out.Finally, here's a new piece from Bindu Ananth that should go on your "must read" list. I couldn't agree with this statement more: "[T]he field of household finance has failed to examine the financial lives of low-income families in sufficient detail." She examines specifically issues with how to think about insurance vs. savings, high frequency saving and borrowing, and financial complexity. I will continue to beat the drum on two points: 1) low-income households are having to make financial decisions that would challenge a finance MBA, with large consequences for sub-optimal choices, and 2) almost all the advice we have on making wise financial choices is built on an assumption that the life-cycle model holds true, and may not in fact be good advice if the life-cycle model doesn't hold.

4. Philanthropy and Systemic Change: Last week I linked to a piece about the return of hookworm in impoverished parts of the US. There's another side of that story: the supposed eradication of hookworm in the American South has long been the benchmark example of philanthropic success (and the gains from the eradication campaign are part of the evidence base for deworming today). Ben Soskis takes a look at what the persistence of hookworm, or the lack of persistence of the eradication campaign, says about the limits of public health philanthropy (or any kind of "systemic change" driven by philanthropy).Here's Felix Salmon reporting from what was apparently definitely not a "premium mediocre" philanthropy conference, where the focus was apparently on "invisible causes and effects." If you have any interest in philanthropic strategy or a bent toward "evidence-based giving" it's worth a read.

5. Household Finance and American Inequality Redux: It's new and old all in the same edition. Here are a couple of things that I wanted to include before they got too out-of-date. First, PWC has a new report on the effects of financial stress on workers. It's almost comically bad, honestly, because they so often seem to miss the story. For instance, while focusing on how self-identified "stressed" workers are likely to withdraw early from their retirement funds (or not have made deposits in the first place), they miss the large percentage of "not stressed" employees who are acting the same way as the stressed ones. When 30% of "not stressed" people already know they are going to need to draw down their retirement savings early, you have a problem with your system.Finally, here's a proposal to allow people to withdraw up to $500 from their Earned Income Tax Credit early in the year to help cope with financial emergencies. Alex Horowitz sounds the proper notes of skepticism on the Federal Government being able to deliver funds in anything like the amount of time that a financial emergency necessitates. One challenge the piece doesn't discuss is that people generally don't know what size their credit is going to be (or even that they qualify for it at all), a challenge exacerbated by income and other household volatility. That's the subject of a paper USFD co-authored with Urban and the topic of a panel next week at the Tax Policy Center. If you're in DC, come along.

1. Digital Finance: There's a regular theme I hit when it comes to digital finance--digital gives much more power to providers, government or private sector, than physical cash does. And that is something we should worry about. So my confirmation bias whet into overdrive when this crossed my feed this week: China is detaining ethnic and religious minorities in Xinjiang Province and one of the criteria for detention is people who "did not use their mobile phone after registering it." Brett Scott objects to cashlessness for both its inherent nature as a tool of surveillance and for more pecuniary reasons: unlike cash, every digital transaction generates fees. Which in turn gives power to the organizations that have a seemingly insatiable appetite for categorizing and controlling people. Hey, ever wonder why Facebook is pushing hard into payments, even into fundraising for non-profits?

Given the near unrelenting negativity above, I feel like I have to say for the record: I don't oppose digitisation. I oppose not recognizing and planning for the negative consequences of digitisation.

2. Global Finance: Digital finance and mobile money is generally about very local transactions. But another important use is long-distance transactions, particularly remittances. But international transfers of funds require banks to have relationships that cross borders. The technical term is "correspondent banks." What correspondent banks do is vastly simplify and accelerate the flow of funds across borders. So it's a problem that correspondent banking relationships are shutting down as a result of "de-risking," which is banking jargon for "avoiding anything that may draw the attention of regulators who have the somewhat arbitrary ability to impose massive fines." The IFC reports that more than a quarter of banks responding to their survey reported losing correspondent bank relationships with compliance costs the most common reason; and 78% expected compliance costs to increase substantially for 2017.

And now for a bit of levity, if you can call it that. Matt Levine has the incredible story of how the Batista brothers, owners of a large Brazilian meat-packing company, made money shorting the Brazilian Real--they knew recordings of their conversations with President Michel Temer about bribes were going to be released. Is that insider trading?

3. US Poverty and Inequality: This week the US Census Bureau released its report on income and poverty in the United States in 2016. The new was good, at least on a relative basis: incomes are growing across the board and poverty is down. But...the majority of gains are still going to upper income groups, and inequality continues to rise as a result. The bottom half of the distribution is only now getting back to where it was in 1999 or earlier. Here's Sheldon Danziger's take on the data and the policy implications. The Economic Progress Institute has a good overview (with good charts) of the poverty data specifically, which focuses on how safety net programs reduce the number of people below poverty by "tens of millions."

The 8+ million who are above the Supplmental Poverty Measure threshold because of refundable tax credits (e.g. the EITC and the Child Tax Credit) particularly caught my eye because of this profile of a US Financial Diaries household that I just finished. Amy Cox, for the year we followed her, is one of those people. For the year, she is above the SPM because of tax credits. But she receives all of that in one lump sum in February. So for 11 months of the year, she's poor. In 9 months of the year, she's around 75% of the SPM threshold. But officially, she's not poor. Makes me think it's time for a Supplemental Supplemental Poverty Measure that takes into account how many weeks a year someone is below the line.

4. Social Investing: Is there any point to avoiding investments in "sin stocks." At least some people think so, giving the proliferation of mutual funds and other investment vehicles that screen companies based on environmental, social or governance criteria (referred to as a category as ESG). Cliff Asness doesn't think so. The summary version (also see Matt Levine) is that if avoiding "sin stocks" causes those companies cost of capital to rise (which is part of the theory of change of many ESG advocates), well that will just increase the returns of those who are willing to invest in sin. If avoiding those stocks doesn't change the cost of capital, then nothing has been accomplished.

5. Education: A few weeks ago I linked to a "Starrant" about Liberia's experimentation with private schools. Last week the preliminary results of the RCT by IPA and CGD that Kevin mentions in his rant were published. There's a little something for everyone here: learning measures were way up, but there was significant heterogeneity among the school operators, and costs were way, way up and those are just the headlines. The biggest question is how to think about the cost-effectiveness, because for instance, this was the first year of the program and it's unclear how much of the increased costs were start-up costs or how scale efficiencies may change the figures.